[404218]: / Code / PennyLane / Data-Reuploading / Learning Rate Studies / 0.52 LR 88.8% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "eI_d8C3_Tnyu",
        "outputId": "d036ea15-a3da-404a-a225-cea0fd159899"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1696829809.5546508\n",
            "Mon Oct  9 05:36:49 2023\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# !pip install pennylane\n",
        "\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lScQSFsjTnyw",
        "outputId": "46086927-e72a-4966-d0c7-abafe506d1fa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "from pennylane.optimize import AdamOptimizer, GradientDescentOptimizer\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Set a random seed\n",
        "np.random.seed(42)\n",
        "\n",
        "\n",
        "# Make a dataset of points inside and outside of a circle\n",
        "def circle(samples, center=[0.0, 0.0], radius=np.sqrt(2 / np.pi)):\n",
        "    \"\"\"\n",
        "    Generates a dataset of points with 1/0 labels inside a given radius.\n",
        "\n",
        "    Args:\n",
        "        samples (int): number of samples to generate\n",
        "        center (tuple): center of the circle\n",
        "        radius (float: radius of the circle\n",
        "\n",
        "    Returns:\n",
        "        Xvals (array[tuple]): coordinates of points\n",
        "        yvals (array[int]): classification labels\n",
        "    \"\"\"\n",
        "    Xvals, yvals = [], []\n",
        "\n",
        "    for i in range(samples):\n",
        "        x = 2 * (np.random.rand(2)) - 1\n",
        "        y = 0\n",
        "        if np.linalg.norm(x - center) < radius:\n",
        "            y = 1\n",
        "        Xvals.append(x)\n",
        "        yvals.append(y)\n",
        "    return np.array(Xvals, requires_grad=False), np.array(yvals, requires_grad=False)\n",
        "\n",
        "\n",
        "def plot_data(x, y, fig=None, ax=None):\n",
        "    \"\"\"\n",
        "    Plot data with red/blue values for a binary classification.\n",
        "\n",
        "    Args:\n",
        "        x (array[tuple]): array of data points as tuples\n",
        "        y (array[int]): array of data points as tuples\n",
        "    \"\"\"\n",
        "    if fig == None:\n",
        "        fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
        "    reds = y == 0\n",
        "    blues = y == 1\n",
        "    ax.scatter(x[reds, 0], x[reds, 1], c=\"red\", s=20, edgecolor=\"k\")\n",
        "    ax.scatter(x[blues, 0], x[blues, 1], c=\"blue\", s=20, edgecolor=\"k\")\n",
        "    ax.set_xlabel(\"$x_1$\")\n",
        "    ax.set_ylabel(\"$x_2$\")\n",
        "\n",
        "\n",
        "Xdata, ydata = circle(500)\n",
        "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
        "plot_data(Xdata, ydata, fig=fig, ax=ax)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# Define output labels as quantum state vectors\n",
        "def density_matrix(state):\n",
        "    \"\"\"Calculates the density matrix representation of a state.\n",
        "\n",
        "    Args:\n",
        "        state (array[complex]): array representing a quantum state vector\n",
        "\n",
        "    Returns:\n",
        "        dm: (array[complex]): array representing the density matrix\n",
        "    \"\"\"\n",
        "    return state * np.conj(state).T\n",
        "\n",
        "\n",
        "label_0 = [[1], [0]]\n",
        "label_1 = [[0], [1]]\n",
        "state_labels = np.array([label_0, label_1], requires_grad=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqAg65FbTnyx"
      },
      "source": [
        "Simple classifier with data reloading and fidelity loss\n",
        "=======================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "ZyKIWD9bTnyx"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"lightning.qubit\", wires=1)\n",
        "# Install any pennylane-plugin to run on some particular backend\n",
        "\n",
        "\n",
        "@qml.qnode(dev, interface=\"autograd\")\n",
        "def qcircuit(params, x, y):\n",
        "    \"\"\"A variational quantum circuit representing the Universal classifier.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): single input vector\n",
        "        y (array[float]): single output state density matrix\n",
        "\n",
        "    Returns:\n",
        "        float: fidelity between output state and input\n",
        "    \"\"\"\n",
        "    for p in params:\n",
        "        qml.Rot(*x, wires=0)\n",
        "        qml.Rot(*p, wires=0)\n",
        "    return qml.expval(qml.Hermitian(y, wires=[0]))\n",
        "\n",
        "\n",
        "def cost(params, x, y, state_labels=None):\n",
        "    \"\"\"Cost function to be minimized.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): 2-d array of input vectors\n",
        "        y (array[float]): 1-d array of targets\n",
        "        state_labels (array[float]): array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        float: loss value to be minimized\n",
        "    \"\"\"\n",
        "    # Compute prediction for each input in data batch\n",
        "    loss = 0.0\n",
        "    dm_labels = [density_matrix(s) for s in state_labels]\n",
        "    for i in range(len(x)):\n",
        "        f = qcircuit(params, x[i], dm_labels[y[i]])\n",
        "        loss = loss + (1 - f) ** 2\n",
        "    return loss / len(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Bz83H0xTnyx"
      },
      "source": [
        "Utility functions for testing and creating batches\n",
        "==================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "i1-TLseuTnyx"
      },
      "outputs": [],
      "source": [
        "def test(params, x, y, state_labels=None):\n",
        "    \"\"\"\n",
        "    Tests on a given set of data.\n",
        "\n",
        "    Args:\n",
        "        params (array[float]): array of parameters\n",
        "        x (array[float]): 2-d array of input vectors\n",
        "        y (array[float]): 1-d array of targets\n",
        "        state_labels (array[float]): 1-d array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        predicted (array([int]): predicted labels for test data\n",
        "        output_states (array[float]): output quantum states from the circuit\n",
        "    \"\"\"\n",
        "    fidelity_values = []\n",
        "    dm_labels = [density_matrix(s) for s in state_labels]\n",
        "    predicted = []\n",
        "\n",
        "    for i in range(len(x)):\n",
        "        fidel_function = lambda y: qcircuit(params, x[i], y)\n",
        "        fidelities = [fidel_function(dm) for dm in dm_labels]\n",
        "        best_fidel = np.argmax(fidelities)\n",
        "\n",
        "        predicted.append(best_fidel)\n",
        "        fidelity_values.append(fidelities)\n",
        "\n",
        "    return np.array(predicted), np.array(fidelity_values)\n",
        "\n",
        "\n",
        "def accuracy_score(y_true, y_pred):\n",
        "    \"\"\"Accuracy score.\n",
        "\n",
        "    Args:\n",
        "        y_true (array[float]): 1-d array of targets\n",
        "        y_predicted (array[float]): 1-d array of predictions\n",
        "        state_labels (array[float]): 1-d array of state representations for labels\n",
        "\n",
        "    Returns:\n",
        "        score (float): the fraction of correctly classified samples\n",
        "    \"\"\"\n",
        "    score = y_true == y_pred\n",
        "    return score.sum() / len(y_true)\n",
        "\n",
        "\n",
        "def iterate_minibatches(inputs, targets, batch_size):\n",
        "    \"\"\"\n",
        "    A generator for batches of the input data\n",
        "\n",
        "    Args:\n",
        "        inputs (array[float]): input data\n",
        "        targets (array[float]): targets\n",
        "\n",
        "    Returns:\n",
        "        inputs (array[float]): one batch of input data of length `batch_size`\n",
        "        targets (array[float]): one batch of targets of length `batch_size`\n",
        "    \"\"\"\n",
        "    for start_idx in range(0, inputs.shape[0] - batch_size + 1, batch_size):\n",
        "        idxs = slice(start_idx, start_idx + batch_size)\n",
        "        yield inputs[idxs], targets[idxs]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5s1nRL2Tnyy"
      },
      "source": [
        "Train a quantum classifier on the circle dataset\n",
        "================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NqJGMjbpTnyy",
        "outputId": "1c172a56-7192-466a-8cd6-86ef5bdd754b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0 | Cost: 0.415535 | Train accuracy: 0.460000 | Test Accuracy: 0.448500\n",
            "Epoch:  1 | Loss: 0.127768 | Train accuracy: 0.835000 | Test accuracy: 0.786500\n",
            "Epoch:  2 | Loss: 0.141633 | Train accuracy: 0.820000 | Test accuracy: 0.787500\n",
            "Epoch:  3 | Loss: 0.123087 | Train accuracy: 0.845000 | Test accuracy: 0.811500\n",
            "Epoch:  4 | Loss: 0.117445 | Train accuracy: 0.840000 | Test accuracy: 0.827500\n",
            "Epoch:  5 | Loss: 0.135384 | Train accuracy: 0.800000 | Test accuracy: 0.795500\n",
            "Epoch:  6 | Loss: 0.113990 | Train accuracy: 0.855000 | Test accuracy: 0.824500\n",
            "Epoch:  7 | Loss: 0.105519 | Train accuracy: 0.870000 | Test accuracy: 0.837500\n",
            "Epoch:  8 | Loss: 0.109992 | Train accuracy: 0.860000 | Test accuracy: 0.823000\n",
            "Epoch:  9 | Loss: 0.122083 | Train accuracy: 0.800000 | Test accuracy: 0.809500\n",
            "Epoch: 10 | Loss: 0.101011 | Train accuracy: 0.915000 | Test accuracy: 0.888000\n"
          ]
        }
      ],
      "source": [
        "# Generate training and test data\n",
        "num_training = 200\n",
        "num_test = 2000\n",
        "\n",
        "Xdata, y_train = circle(num_training)\n",
        "X_train = np.hstack((Xdata, np.zeros((Xdata.shape[0], 1), requires_grad=False)))\n",
        "\n",
        "Xtest, y_test = circle(num_test)\n",
        "X_test = np.hstack((Xtest, np.zeros((Xtest.shape[0], 1), requires_grad=False)))\n",
        "\n",
        "\n",
        "# Train using Adam optimizer and evaluate the classifier\n",
        "num_layers = 3\n",
        "learning_rate = 0.52\n",
        "epochs = 10\n",
        "batch_size = 32\n",
        "\n",
        "opt = AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999)\n",
        "\n",
        "# initialize random weights\n",
        "params = np.random.uniform(size=(num_layers, 3), requires_grad=True)\n",
        "\n",
        "predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
        "accuracy_train = accuracy_score(y_train, predicted_train)\n",
        "\n",
        "predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
        "accuracy_test = accuracy_score(y_test, predicted_test)\n",
        "\n",
        "# save predictions with random weights for comparison\n",
        "initial_predictions = predicted_test\n",
        "\n",
        "loss = cost(params, X_test, y_test, state_labels)\n",
        "\n",
        "print(\n",
        "    \"Epoch: {:2d} | Cost: {:3f} | Train accuracy: {:3f} | Test Accuracy: {:3f}\".format(\n",
        "        0, loss, accuracy_train, accuracy_test\n",
        "    )\n",
        ")\n",
        "\n",
        "for it in range(epochs):\n",
        "    for Xbatch, ybatch in iterate_minibatches(X_train, y_train, batch_size=batch_size):\n",
        "        params, _, _, _ = opt.step(cost, params, Xbatch, ybatch, state_labels)\n",
        "\n",
        "    predicted_train, fidel_train = test(params, X_train, y_train, state_labels)\n",
        "    accuracy_train = accuracy_score(y_train, predicted_train)\n",
        "    loss = cost(params, X_train, y_train, state_labels)\n",
        "\n",
        "    predicted_test, fidel_test = test(params, X_test, y_test, state_labels)\n",
        "    accuracy_test = accuracy_score(y_test, predicted_test)\n",
        "    res = [it + 1, loss, accuracy_train, accuracy_test]\n",
        "    print(\n",
        "        \"Epoch: {:2d} | Loss: {:3f} | Train accuracy: {:3f} | Test accuracy: {:3f}\".format(\n",
        "            *res\n",
        "        )\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWspC-9BTnyy"
      },
      "source": [
        "Results\n",
        "=======\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "ZPszGYA3Tnyy",
        "outputId": "b7057b74-0523-49ac-b57a-29d61e619697"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost: 0.101011 | Train accuracy 0.915000 | Test Accuracy : 0.888000\n",
            "Learned weights\n",
            "Layer 0: [-0.7660987   1.54116275 -0.05016394]\n",
            "Layer 1: [0.96828026 0.31895859 0.11165582]\n",
            "Layer 2: [ 2.21444869 -1.35556556  0.32099704]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\n",
        "    \"Cost: {:3f} | Train accuracy {:3f} | Test Accuracy : {:3f}\".format(\n",
        "        loss, accuracy_train, accuracy_test\n",
        "    )\n",
        ")\n",
        "\n",
        "print(\"Learned weights\")\n",
        "for i in range(num_layers):\n",
        "    print(\"Layer {}: {}\".format(i, params[i]))\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
        "plot_data(X_test, initial_predictions, fig, axes[0])\n",
        "plot_data(X_test, predicted_test, fig, axes[1])\n",
        "plot_data(X_test, y_test, fig, axes[2])\n",
        "axes[0].set_title(\"Predictions with random weights\")\n",
        "axes[1].set_title(\"Predictions after training\")\n",
        "axes[2].set_title(\"True test data\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYQWz6IdTnyy"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "\\[1\\] Pérez-Salinas, Adrián, et al. \\\"Data re-uploading for a universal\n",
        "quantum classifier.\\\" arXiv preprint arXiv:1907.02085 (2019).\n",
        "\n",
        "\\[2\\] Kingma, Diederik P., and Ba, J. \\\"Adam: A method for stochastic\n",
        "optimization.\\\" arXiv preprint arXiv:1412.6980 (2014).\n",
        "\n",
        "\\[3\\] Liu, Dong C., and Nocedal, J. \\\"On the limited memory BFGS method\n",
        "for large scale optimization.\\\" Mathematical programming 45.1-3 (1989):\n",
        "503-528.\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8FGVwF_QUYza",
        "outputId": "33b8f78c-37ec-4178-e691-e06ecfc417c1"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1696830086.422844\n",
            "Mon Oct  9 05:41:26 2023\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.18"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}